"""
Filename: ResNet18.py
Author: Deng Weiwei
Description: resnet18
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, mid_channels, stride=1, resolution=None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(mid_channels)
        self.conv2 = nn.Conv2d(mid_channels, mid_channels, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(mid_channels)

        self.residual = nn.Sequential()
        if stride != 1 or in_channels != mid_channels:
            self.residual = nn.Sequential(
                nn.Conv2d(in_channels, mid_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(mid_channels)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.residual(x)
        out = F.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes, resolution):
        super(ResNet, self).__init__()
        self.in_channels = 64
        self.resolution = list(resolution)

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.resolution[0] /= 2
        self.resolution[1] /= 2

        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.resolution[0] /= 2
        self.resolution[1] /= 2

        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Sequential(
            nn.Dropout(0.3),
            nn.Linear(512 * block.expansion, num_classes)
        )

    def _make_layer(self, block, mid_channels, num_blocks, stride=1):
        layers = []

        layers.append(block(self.in_channels, mid_channels, stride, self.resolution.copy()))

        if stride == 2:
            self.resolution[0] /= 2
            self.resolution[1] /= 2

        self.in_channels = mid_channels
        for _ in range(1, num_blocks):
            layers.append(block(self.in_channels, mid_channels,1, self.resolution.copy()))
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.maxpool(out)

        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)

        out = self.avgpool(out)
        out = torch.flatten(out, 1)
        out = self.fc(out)
        return out

def ResNet18(num_classes=100, resolution=(32, 32)):
    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, resolution=resolution)

if __name__ == "__main__":
    net = ResNet18().to('cuda')
    summary(net, input_size=(3, 32, 32), device='cuda')